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      orthoFind Facilitates the Discovery of Homologous and Orthologous Proteins

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          Abstract

          Finding homologous and orthologous protein sequences is often the first step in evolutionary studies, annotation projects, and experiments of functional complementation. Despite all currently available computational tools, there is a requirement for easy-to-use tools that provide functional information. Here, a new web application called orthoFind is presented, which allows a quick search for homologous and orthologous proteins given one or more query sequences, allowing a recurrent and exhaustive search against reference proteomes, and being able to include user databases. It addresses the protein multidomain problem, searching for homologs with the same domain architecture, and gives a simple functional analysis of the results to help in the annotation process. orthoFind is easy to use and has been proven to provide accurate results with different datasets. Availability: http://www.bioinfocabd.upo.es/orthofind/.

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          eggNOG v4.0: nested orthology inference across 3686 organisms

          Sean Powell, Kristoffer Forslund, Damian Szklarczyk … (2013)
          With the increasing availability of various ‘omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at http://eggnog.embl.de) that derives nonsupervised orthologous groups (NOGs) from complete genomes, and then applies a comprehensive characterization and analysis pipeline to the resulting gene families. Compared with the previous version, we have more than tripled the underlying species set to cover 3686 organisms, keeping track with genome project completions while prioritizing the inclusion of high-quality genomes to minimize error propagation from incomplete proteome sets. Major technological advances include (i) a robust and scalable procedure for the identification and inclusion of high-quality genomes, (ii) provision of orthologous groups for 107 different taxonomic levels compared with 41 in eggNOGv3, (iii) identification and annotation of particularly closely related orthologous groups, facilitating analysis of related gene families, (iv) improvements of the clustering and functional annotation approach, (v) adoption of a revised tree building procedure based on the multiple alignments generated during the process and (vi) implementation of quality control procedures throughout the entire pipeline. As in previous versions, eggNOGv4 provides multiple sequence alignments and maximum-likelihood trees, as well as broad functional annotation. Users can access the complete database of orthologous groups via a web interface, as well as through bulk download.
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            The quest for orthologs: finding the corresponding gene across genomes.

            Sándor Pongor, Jack A. M. Leunissen, Andries J van Tonder … (2008)
            Orthology is a key evolutionary concept in many areas of genomic research. It provides a framework for subjects as diverse as the evolution of genomes, gene functions, cellular networks and functional genome annotation. Although orthologous proteins usually perform equivalent functions in different species, establishing true orthologous relationships requires a phylogenetic approach, which combines both trees and graphs (networks) using reliable species phylogeny and available genomic data from more than two species, and an insight into the processes of molecular evolution. Here, we evaluate the available bioinformatics tools and provide a set of guidelines to aid researchers in choosing the most appropriate tool for any situation.
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              Computational methods for Gene Orthology inference.

              Maxim Wolf, Eugene V. Koonin, Arcady R Mushegian … (2011)
              Accurate inference of orthologous genes is a pre-requisite for most comparative genomics studies, and is also important for functional annotation of new genomes. Identification of orthologous gene sets typically involves phylogenetic tree analysis, heuristic algorithms based on sequence conservation, synteny analysis, or some combination of these approaches. The most direct tree-based methods typically rely on the comparison of an individual gene tree with a species tree. Once the two trees are accurately constructed, orthologs are straightforwardly identified by the definition of orthology as those homologs that are related by speciation, rather than gene duplication, at their most recent point of origin. Although ideal for the purpose of orthology identification in principle, phylogenetic trees are computationally expensive to construct for large numbers of genes and genomes, and they often contain errors, especially at large evolutionary distances. Moreover, in many organisms, in particular prokaryotes and viruses, evolution does not appear to have followed a simple 'tree-like' mode, which makes conventional tree reconciliation inapplicable. Other, heuristic methods identify probable orthologs as the closest homologous pairs or groups of genes in a set of organisms. These approaches are faster and easier to automate than tree-based methods, with efficient implementations provided by graph-theoretical algorithms enabling comparisons of thousands of genomes. Comparisons of these two approaches show that, despite conceptual differences, they produce similar sets of orthologs, especially at short evolutionary distances. Synteny also can aid in identification of orthologs. Often, tree-based, sequence similarity- and synteny-based approaches can be combined into flexible hybrid methods.
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                Author and article information

                Contributors
                Narcis Fernandez-Fuentes: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 1 December 2015
                Publication date Collection: 2015
                Volume: 10
                Issue: 12
                Electronic Location Identifier: e0143906
                Affiliations
                [1 ]Centro Andaluz de Biología del Desarrollo (CABD), CSIC-UPO-JA. Facultad de Ciencias Experimentales (Área de Genética), Universidad Pablo de Olavide, ES-41013, Sevilla, Spain
                [2 ]Faculty of Biology, Johannes Gutenberg University Mainz, Mainz, Germany
                [3 ]Institute of Molecular Biology, Mainz, Germany
                Aberystwyth University, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: AJP. Performed the experiments: PM. Analyzed the data: PM MAA AJP. Contributed reagents/materials/analysis tools: PM MAA AJP. Wrote the paper: AJP.

                Article
                Publisher ID: PONE-D-15-29951
                DOI: 10.1371/journal.pone.0143906
                PMC ID: 4666658
                PubMed ID: 26624019
                SO-VID: e001c0b8-f8f5-40a0-9408-e102c88380fb
                Copyright statement: Copyright @ 2015
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                Date received : 8 July 2015
                Date accepted : 7 October 2015
                Page count
                Figures: 5, Tables: 1, Pages: 13
                Funding
                This work has been funded by the Spanish “Ministerio de Economia y Competitividad” within the BFU2013-46923-P project.
                Categories
                Subject: Research Article
                Custom metadata
                Data Availability All relevant data are within the paper and its Supporting Information files.

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

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